The inherent advantages of having perfect knowledge of the future in resource management is obvious in that if a resource manager can know exactly how much a limited resource will be available at a given time in the future, and how much demand for that resource that needs to be satisfied at that time, then the best possible decision can be made so to maximise economic benefits that would result from the optimal use of that available resource to satisfy that demand.
In real-life practice, however, having perfect knowledge of the future is not a present reality, and resource managers have to rather rely on modeling and predictions as to the future availability of resources as well as the future demand for or expenditures of the resources.
The number of approaches and methodologies for generating predictions of the future is diversely plentiful, but one commonality amongst all of them is that they can only generate estimates based on imperfect knowledge of the future at the time of prediction.
For operations or systems of any complexity, computer modeling is a convenient means that resource managers can use to generate a mathematical representation (model) of a given operation or system. Depending on complexity, a system can comprise of many interacting subsystems, with the corresponding system model comprising of many interacting submodels.
The representation of the system at a given time is commonly referred to as the “state” of the system for that time. The modeled system would take into account a plethora of factors (or variables) that influences and constrains the modeled system, and it is the changes in these variables over time that evolves the system model from one mathematical state to another mathematical state (hence the term “state variables”). The time interval between one state of the modeled system to another state in computer simulation is commonly referred to as a “stage” or a “time-step”.
Some of these state variables are dependent variables inherent within the modeled system, while others are independent variables that can include external influences and/or constraints that have to be inputted into the modeled system. In resource management modeling, one or more state variables could represent the level or amount of one or more resources of interest to the resource manager, while other state variable(s) of the model could be the level or amount of demand for such resource(s). It is the changes in these state variables across time-steps in a modeled system that provide a resource manager with the information required for making decisions as to how best to valuate, allocate, and deploy, the resource(s) available to satisfy demand at a given time and maximize economic benefits that would result from same.
When a resource manager is using a system model to determine future availability of, or demand for, a given resource without perfect foreknowledge, at least some of these input variables would have to be predictions based on imperfect knowledge of the future and made using any of the aforementioned diverse selection of forecast methods. Of course, when any of the input variables are predictions or estimates based on imperfect knowledge of the future, they would be subject to inherent uncertainty and inaccuracy. Consequently, the output state variables in terms of resource availability and/or demand would likewise contain inherent uncertainty and inaccuracy. As such, any valuation of resources and any decision made based on any erroneous information would be suboptimal, and the deleterious effects of making suboptimal valuation and/or decisions repeatedly over a number of stages or time-steps can be amplified and have profound adversity on the overall economic benefits that would be realized.
Resource management models can be deterministic or stochastic. Deterministic models require that all future inputs need to be supplied with “certainty” for the formulation of the model problem; they also called scenario planning models. Stochastic (or probabilistic) models, on the other hand, directly accommodate “uncertainty” of inputs by requiring a probabilistic representation (e.g. statistical distribution) of every uncertain input and can thereby generate outputs given with confidence intervals.
As mentioned above, resource management modeling of operations or systems commonly requires the formulation of large number of influences and constraints. This requirement can significantly limit the selection of available mathematical programming techniques that can perform the task, and it can also place an inordinate amount of computational burden and modeling time. Due to these reasons, a deterministic problem formulation is oftentimes used for resource management modeling of large and/or complex operations or systems, although stochastic modeling is also becoming increasingly viable with our continuing advances in computational technologies.
Regardless of deterministic or stochastic modeling, either situation would still require formulation of input scenarios (considering for all influences and constants) to provide a vision of the future; and when input scenarios (whether finite or probabilistic) are simply predictions based on imperfect foreknowledge, the resulting error within the predicted outputs, such as availability of resources or future demand, can mislead the resource manager to assume inappropriate risk levels and thereby resulting in decisions that produce unrealistic net benefits and/or costs.
In reality all time-related planning processes must in some fashion consider time-related uncertainties in order to improve decision making. A number of the more sophisticated resource management models have the programmed ability to automatically provide recommendations to the resource manager on how best to valuate, allocate, and deploy, resources of interest to activities that would maximize economic benefit and/or minimize cost over planning periods. These models can be applied to planning the operation and expansion of assets and resource in many industries including complex electrical utility systems, and they have to take into consideration a tremendous variety and number of state variables, notwithstanding the added algorithms to enable automated formulation of decision(s) based on applicable state variables at each time-step. The accuracy and actual value of the any decision made, regardless of level of sophistication of the decision making algorithms, would hinge upon the accuracy of the input state variables and cannot escape the inevitable “garbage-in-garbage-out” idiom. That said, the selection of and reliance on suboptimal decision formulation methodology, regardless of however perfect and accurate the predictions are, would still yield suboptimal decisions and compromised economic benefits.
Similar to the fact that there are many different methods for making predictions on state variables, there are also diverse approaches and methods for formulation of decisions based on any given state of a system. Oftentimes, a resource manager may use different decision formulation methodologies and algorithms to variably compensate for the suspected degrees of error caused by inaccurate prediction of input state variables, and as such, very different decisions (e.g. in terms of course of action respecting valuation, allocation, or deployment, of available resources) can be reached even for a single state of the modeled system.
At the end, with the multitude of approaches and methodologies for both prediction of input state variables, matrixed with the multitude of approaches and methodologies for decision formulation (including resource valuation), resource managers are commonly left with the question as to which combination of prediction method vs. decision formulation method should be adopted/matched so that economic benefits are indeed maximized.
An examination of the prior art has revealed numerous different methods that resource managers can use to evaluate a given prediction method or a given decision formulation method, but this piecemeal approach would be time consuming, and perhaps more importantly, it cannot be used to holistically or systemically evaluate different combinations and permutations of prediction methods and decision formulation methods. Further, a resource manager would now be saddled with yet another layer of uncertainty in decision-making in terms of which evaluation methodology would be best and should be used, and the quagmire worsens.
Yet further, resource managers are often required to plan for different time frames or time scales (e.g. hourly, weekly, monthly, and multiyear planning), and as such, their operations and systems are simulated using models that run on different time horizons and time-steps. Ultimately, at any given point in time, the longer-term predictions in terms of resource availability and resource demand would feed into the shorter term predictions which would eventually drive present-time evaluation of resource, formulation of decisions on resource allocation and deployment, and execution of resulting decisions to generate corresponding economic benefits.
As such, it is important that approaches and methods used for predicting resource availability in the more distant future are comparable and compatible (e.g. in terms of method bias and accuracy) with approaches and methods used for predicting resource availability in the near future. At a given demand level, an over-abundance of resource can lead to under-valuation of said resource and vice versa, and any mismatch in valuation of a given resource at the boundary between two different time frames would be confusing and not very helpful for the resource manager.
So in order to improve the utility of the economic results from deterministic modeling, there is therefore a need for a more practicable and more universal solution that can help resource managers meaningfully evaluate and select optimal methods for prediction as well as methods of decision formulation for their respective situations and purposes.